张量流keras模型的动态历元数 [英] Dynamic number of epochs with a tensorflow keras model
本文介绍了张量流keras模型的动态历元数的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我希望有一个神经网络进行训练,直到达到一定程度的准确性为止.是否有内置功能可以使用,而不是单独运行每个时期直到达到精度?
I want to have a neural net that trains until a certain level of accuracy has been reached. Is there a built in function to use instead of running each epoch individually until the accuracy has been reached?
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
epochs = 0
train_acc = 0
while 1-train_acc > .01:
model.fit(train_images, train_labels, initial_epoch=epochs, epochs=epochs+1,verbose=0)
epochs += 1
train_loss, train_acc = model.evaluate(train_images,train_labels)
推荐答案
不,没有任何内置函数可以执行此操作.但是,您可以轻松定义一个自定义回调,一旦训练精度达到一定阈值,该回调便停止训练:
No, there isn't any built in function to do this. However, you can easily define a custom callback that stops training once the training accuracy reaches a certain threshold:
import keras
class AccuracyStopping(keras.callbacks.Callback):
def __init__(self, acc_threshold):
super(AccuracyStopping, self).__init__()
self._acc_threshold = acc_threshold
def on_epoch_end(self, batch, logs={}):
train_acc = logs.get('acc')
self.model.stop_training = 1 - train_acc <= self._acc_threshold
这是一个简单的示例,展示了如何使用它:
Here's a simple example showing how to use it:
import numpy as np
from keras.layers import Dense
from keras.models import Sequential
x = np.random.normal(size=(100,))
y = x > 0
model = Sequential()
model.add(Dense(1, input_dim=1, activation='sigmoid'))
model.compile('sgd', 'binary_crossentropy', metrics=['accuracy'])
acc_callback = AccuracyStopping(0.05)
model.fit(x, y, batch_size=8, epochs=1000, callbacks=[acc_callback])
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